mlsanityData to 7 Oct 2026

CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature

arXiv 2406.09128 ↗First posted 13 Jun 2024cs.CLcited by 1

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Abstract (arXiv)

The growing impact of climate change on coastal areas, particularly active but fragile regions, necessitates collaboration among diverse stakeholders and disciplines to formulate effective environmental protection policies. We introduce a novel specialized corpus comprising 2,491 sentences from 410 scientific abstracts concerning coastal areas, for the Automatic Term Extraction (ATE) and Classification (ATC) tasks. Inspired by the ARDI framework, focused on the identification of Actors, Resources, Dynamics and Interactions, we automatically extract domain terms and their distinct roles in the functioning of coastal systems by leveraging monolingual and multilingual transformer models. The evaluation demonstrates consistent results, achieving an F1 score of approximately 80% for automated term extraction and F1 of 70% for extracting terms and their labels. These findings are promising and signify an initial step towards the development of a specialized Knowledge Base dedicated to coastal areas.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 40 references that are in the map. First posted on arXiv 13 Jun 2024.

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5 of 40 references.

References · 5

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2017SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publicationscited by 101
  4. 2023The Recent Advances in Automatic Term Extraction: A surveycited by 4
  5. 2022Ensembling Transformers for Cross-domain Automatic Term Extractioncited by 2

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